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1. An open-source platform for sub-$$\textrm{g}$$, sub-$$\upmu$$A data loggers

   https://doi.org/10.1186/s40317-023-00327-0  

   Brown, Geoffrey M. ; Chen, Jiawei ; Fudickar, Adam ; Jahn, Alex E. ( May 2023 , Animal Biotelemetry)

   Rapid improvements in inexpensive, low-power, movement and environmental sensors have sparked a revolution in animal behavior research by enabling the creation of data loggers (henceforth, tags) that can capture fine-grained behavioral data over many months. Nevertheless, development of tags that are suitable for use with small species, for example, birds under 25 g, remains challenging because of the extreme mass (under 1$$\textrm{g}$$$\text{g}$) and power (average current under 1$$\upmu$$$\mu$A) constraints. These constraints dictate that a tag should carry exactly the sensors required for a given experiment and the data collection protocol should be specialized to the experiment. Furthermore, it can be extremely challenging to design hardware and software to achieve the energy efficiency required for long tag life.

   We present an activity monitor, BitTag, that can continuously collect activity data for 4–12 months at 0.5–0.8$$\textrm{g}$$$\text{g}$, depending upon battery choice, and which has been used to collect more than 500,000 h of data in a variety of experiments. The BitTag architecture provides a general platform to support the development and deployment of custom sub-$$\textrm{g}$$$\text{g}$tags. This platform consists of a flexible tag architecture, software for both tags and host computers, and hardware to provide the host/tag interface necessary for preparing tags for “flight” and for accessing tag data “post-flight”. We demonstrate how the BitTag platform can be extended to quickly develop novel tags with other sensors while satisfying the 1g/1$$\upmu$$$\mu$A mass and power requirements through the design of a novel barometric pressure sensing tag that can collect pressure and temperature data every 60$$\textrm{s}$$$\text{s}$for a year with mass under 0.6$$\textrm{g}$$$\text{g}$.

    

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2. Identification of all-against-all protein–protein interactions based on deep hash learning

   https://doi.org/10.1186/s12859-022-04811-x  

   Jiang, Yue ; Wang, Yuxuan ; Shen, Lin ; Adjeroh, Donald A. ; Liu, Zhidong ; Lin, Jie ( July 2022 , BMC Bioinformatics)

   Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wet-lab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.

   In this work, we propose a more efficient model, called deep hash learning protein-and-protein interaction (DHL-PPI), to predict all-against-all PPI relationships in a database of proteins. First, DHL-PPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the pre-screening stage of DHL-PPI, the string matching problem of comparing a protein sequence against a database withMproteins can be transformed into a much more simpler problem: to find a number inside a sorted array of lengthM. This pre-screening process narrows down the search to a much smaller set of candidate proteins for further confirmation. As a final step, DHL-PPI uses the Hamming distance to verify the final PPI relationship.

   The experimental results confirmed that DHL-PPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHL-PPI is shown to be superior or competitive when compared to the other state-of-the-art methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHL-PPI reduced the time complexity from$$O(M^2)$$$O\left(M^2\right)$to$$O(M\log M)$$$O(MlogM)$for performing an all-against-all PPI prediction for a database withMproteins. With the proposed approach, a protein database can be preprocessed and stored for later search using the proposed encoding scheme. This can provide a more efficient way to cope with the rapidly increasing volume of protein datasets.

    

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3. Searching for a dark matter particle with anti-protonic atoms

   https://doi.org/10.1140/epjc/s10052-023-12319-8  

   Doser, Michael ; Farrar, Glennys ; Kornakov, Georgy ( December 2023 , The European Physical Journal C)

   A wide range of dark matter candidates have been proposed and are actively being searched for in a large number of experiments, both at high (TeV) and low (sub meV) energies. One dark matter candidate, a deeply bounduuddsssexaquark,$$S$$$S$, with mass$$\sim 2$$$\sim 2$GeV (having the same quark content as the hypothesized H-dibaryon, but long lived) is particularly difficult to explore experimentally. In this paper, we propose a scheme in which such a state could be produced at rest through the formation of$$\bar{p}$$$\overline{p}$–$$^3$$${}^3$He antiprotonic atoms and their annihilation into$$S$$$S$+$$K^+K^+\pi ^-$$$K^{+}{K}^{+}{\pi }^{-}$, identified both through the unique tag of a$$S=+2, Q=+1$$$S=+2,Q=+1$final state, as well as through full kinematic reconstruction of the final state recoiling against it.

    

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4. A Strong Hybrid Fatty Acid Inducible Transcriptional Sensor Built From Yarrowia lipolytica Upstream Activating and Regulatory Sequences

   https://doi.org/10.1002/biot.201700248  

   Shabbir Hussain, Murtaza ; Wheeldon, Ian ; Blenner, Mark A. ( September 2017 , Biotechnology Journal)

   The engineering ofYarrowia lipolyticato accumulate lipids with high titers and productivities has been enabled with a handful of constitutive promoters for pathway engineering. However, the development of promoters that are both strong and lipid responsive could greatly benefit the bioproduction efficiency of lipid‐derived oleochemicals in oleaginous yeast. In this study, a fatty acid regulated hybrid promoter for use inY. lipolyticais engineered. A 200 bp upstream regulatory sequence in the peroxisomal acyl CoA oxidase 2 (POX2) promoter is identified. Further analysis of the promoter sequence reveal a regulatory sequence, that when used in tandem repeats, lead to a 48‐fold induction of gene expression relative to glucose and fourfold higher than the native POX2 promoter. To date, this is the strongest inducible promoter reported inY. lipolytica. Taken together, the results show that it is possible to engineer strong promoters that retain strong inducibility. These types of promoters will be useful in controlling metabolism and as fatty acid sensors.

    

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5. Locality-sensitive bucketing functions for the edit distance

   https://doi.org/10.1186/s13015-023-00234-2  

   Chen, Ke ; Shao, Mingfu ( July 2023 , Algorithms for Molecular Biology)

   Many bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existingk-mer-based bucketing methods have been efficient in processing sequencing data with low error rates, but encounter much reduced sensitivity on data with high error rates. Locality-sensitive hashing (LSH) schemes are able to mitigate this issue through tolerating the edits in similar sequences, but state-of-the-art methods still have large gaps.

   In this paper, we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be$$(d_1, d_2)$$$(d_1,d_2)$-sensitive if any two sequences within an edit distance of$$d_1$$$d_1$are mapped into at least one shared bucket, and any two sequences with distance at least$$d_2$$$d_2$are mapped into disjoint subsets of buckets. We construct locality-sensitive bucketing (LSB) functions with a variety of values of$$(d_1,d_2)$$$(d_1,d_2)$and analyze their efficiency with respect to the total number of buckets needed as well as the number of buckets that a specific sequence is mapped to. We also prove lower bounds of these two parameters in different settings and show that some of our constructed LSB functions are optimal.

   These results lay the theoretical foundations for their practical use in analyzing sequences with high error rates while also providing insights for the hardness of designing ungapped LSH functions.

    

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